Vibration-based inverse algorithms for detection of delamination in composites

Zhang, Zhifang; Shankar, Krishna; Ray, Tapabrata; Морозов, Е.В.; Tahtalı, Murat

doi:10.1016/j.compstruct.2013.03.012

Cited by 92 publications

(43 citation statements)

References 25 publications

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“…4.2). Few studies, which did not mention their training and testing data ratio, were mostly numerical simulation based experiments [41,68]. This is because simulated models offer users a large working domain as well as provide flexibility and variation in data collection.…”

Section: System Identificationmentioning

confidence: 99%

Artificial neural networks for vibration based inverse parametric identifications: A review

Hossain

Ong

Ismail

et al. 2017

Applied Soft Computing

102

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Graphical abstract2 Highlights  ANNs-solved vibration based parametric identification studies are reviewed. Factors which affect identification result are discussed. Pros and cons of ANN approaches are mentioned. 1• Fundamentals of ANNs. 2• Dynamic behavior analysis for parametric identifications.• Reason for adopting ANNs to vibrational inverse identifications. 3• Earlier ANN approaches to different vibrational parametric identifications.--Signal pre-processing techniques --Input-output schemes --ANN models 4• Factors that affect ANNs performance for vibrational paramteric identification. 5• Advantages and disadvantages of ANN approaches. 6• Suggestions to potential researchers.• Experimental validation of authors' suggestion based on the literature.3  Suggestions are given to potential researchers based on the discussion. Analysis with experimental results is provided to justify some point of view. AbstractVibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNs' prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods. Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes.

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Section: System Identificationmentioning

confidence: 99%

Artificial neural networks for vibration based inverse parametric identifications: A review

Hossain

Ong

Ismail

et al. 2017

Applied Soft Computing

102

View full text Add to dashboard Cite

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“…The authors concluded that both algorithms were able to correctly identify the delamination, but neural networks are more stable. Zhang et al [18,19] compared three inverse algorithms to predict the location and size of delamination in a composite beam, namely, a graphical technique, neural networks, and surrogate-assisted optimization. The three algorithms could predict the delamination parameters, but neural networks were found to be less reliable in the presence of experimental noise.…”

Section: Introductionmentioning

confidence: 99%

Modal Strain Energy-Based Debonding Assessment of Sandwich Panels Using a Linear Approximation with Maximum Entropy

Meruane

Lasen

Droguett

et al. 2017

Entropy

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Sandwich structures are very attractive due to their high strength at a minimum weight, and, therefore, there has been a rapid increase in their applications. Nevertheless, these structures may present imperfect bonding or debonding between the skins and core as a result of manufacturing defects or impact loads, degrading their mechanical properties. To improve both the safety and functionality of these systems, structural damage assessment methodologies can be implemented. This article presents a damage assessment algorithm to localize and quantify debonds in sandwich panels. The proposed algorithm uses damage indices derived from the modal strain energy method and a linear approximation with a maximum entropy algorithm. Full-field vibration measurements of the panels were acquired using a high-speed 3D digital image correlation (DIC) system. Since the number of damage indices per panel is too large to be used directly in a regression algorithm, reprocessing of the data using principal component analysis (PCA) and kernel PCA has been performed. The results demonstrate that the proposed methodology accurately identifies debonding in composite panels.

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“…Nonparametric algorithms do not assume a structure for the data. The most frequently nonparametric algorithms used in damage assessment are artificial neural networks [18][19][20][21][22][23][24]. A trained neural network can potentially detect, locate and quantify structural damage in a short period of time.…”

Section: Introductionmentioning

confidence: 99%

A Maximum Entropy Approach to Assess Debonding in Honeycomb aluminum Plates

Meruane

Fierro

Ortiz-Bernardin

2014

Entropy

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Honeycomb sandwich structures are used in a wide variety of applications. Nevertheless, due to manufacturing defects or impact loads, these structures can be subject to imperfect bonding or debonding between the skin and the honeycomb core. The presence of debonding reduces the bending stiffness of the composite panel, which causes detectable changes in its vibration characteristics. This article presents a new supervised learning algorithm to identify debonded regions in aluminum honeycomb panels. The algorithm uses a linear approximation method handled by a statistical inference model based on the maximum-entropy principle. The merits of this new approach are twofold: training is avoided and data is processed in a period of time that is comparable to the one of neural networks. The honeycomb panels are modeled with finite elements using a simplified three-layer shell model. The adhesive layer between the skin and core is modeled using linear springs, the rigidities of which are reduced in debonded sectors. The algorithm is validated using experimental data of an aluminum honeycomb panel under different damage scenarios.

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Vibration-based inverse algorithms for detection of delamination in composites

Cited by 92 publications

References 25 publications

Artificial neural networks for vibration based inverse parametric identifications: A review

Artificial neural networks for vibration based inverse parametric identifications: A review

Modal Strain Energy-Based Debonding Assessment of Sandwich Panels Using a Linear Approximation with Maximum Entropy

A Maximum Entropy Approach to Assess Debonding in Honeycomb aluminum Plates

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